Hyperspectral optical imaging of human iris in vivo: characteristics of reflectance spectra

Topical Review: Studies of Ocular Function and Disease Using Hyperspectral Imaging

SIGNIFICANCE

Advances in imaging technology over the last two decades have produced significant innovations in medical imaging. Hyperspectral imaging (HSI) is one of these innovations, enabling powerful new imaging tools for clinical use and greater understanding of tissue optical properties and mechanisms underlying eye disease.Hyperspectral imaging is an important and rapidly growing area in medical imaging, making possible the concurrent collection of spectroscopic and spatial information that is usually obtained from separate optical recordings. In this review, we describe several mainstream techniques used in HSI, along with noteworthy advances in optical technology that enabled modern HSI techniques. Presented also are recent applications of HSI for basic and applied eye research, which include a novel method for assessing dry eye syndrome, clinical slit-lamp examination of corneal injury, measurement of blood oxygen saturation in retinal disease, molecular changes in macular degeneration, and detection of early stages of Alzheimer disease. The review also highlights work resulting from integration of HSI with other imaging tools such as optical coherence tomography and autofluorescence microscopy and discusses the adaptation of HSI for clinical work where eye motion is present. Here, we present the background and main findings from each of these reports along with specific references for additional details.

Design and performance of a hyperspectral imaging system: Preliminary in vivo spectral reflectance measurements of the human iris

In this paper, we present a novel hyperspectral system designed for imaging the human iris in vivo. The instrument is based on a tunable light source (from 480 nm to 900 nm) and a monochrome camera. The system can acquire hyperspectral cubes in 4 s, with spatial and spectral resolutions of 11 μm and 20 nm, respectively. The optical characterization of the new instrument is described, and we demonstrate its safety with respect to ANSI standards. Preliminary in vivo measurements in human volunteers are also presented.

Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform

Many spectral responses in materials science, physics, and chemistry experiments can be characterized as resulting from the superposition of a number of more basic individual spectra. In this context, unmixing is defined as the problem of determining the individual spectra, given measurements of multiple spectra that are spatially resolved across samples, as well as the determination of the corresponding abundance maps indicating the local weighting of each individual spectrum. Matrix factorization is a popular linear unmixing technique that considers that the mixture model between the individual spectra and the spatial maps is linear. Here, we present a tutorial paper targeted at domain scientists to introduce linear unmixing techniques, to facilitate greater understanding of spectroscopic imaging data. We detail a matrix factorization framework that can incorporate different domain information through various parameters of the matrix factorization method. We demonstrate many domain-specific examples to explain the expressivity of the matrix factorization framework and show how the appropriate use of domain-specific constraints such as non-negativity and sum-to-one abundance result in physically meaningful spectral decompositions that are more readily interpretable. Our aim is not only to explain the off-the-shelf available tools, but to add additional constraints when ready-made algorithms are unavailable for the task. All examples use the scalable open source implementation from https://github.com/ramkikannan/nmflibrary that can run from small laptops to supercomputers, creating a user-wide platform for rapid dissemination and adoption across scientific disciplines.

Hyperspectral interventional imaging for enhanced tissue visualization and discrimination combining band selection methods

PURPOSE

Hyperspectral imaging is an emerging technology recently introduced in medical applications inasmuch as it provides a powerful tool for noninvasive tissue characterization. In this context, a new system was designed to be easily integrated in the operating room in order to detect anatomical tissues hardly noticed by the surgeon's naked eye.

METHOD

Our LCTF-based spectral imaging system is operative over visible, near- and middle-infrared spectral ranges (400-1700 nm). It is dedicated to enhance critical biological tissues such as the ureter and the facial nerve. We aim to find the best three relevant bands to create a RGB image to display during the intervention with maximal contrast between the target tissue and its surroundings. A comparative study is carried out between band selection methods and band transformation methods. Combined band selection methods are proposed. All methods are compared using different evaluation criteria.

RESULTS

Experimental results show that the proposed combined band selection methods provide the best performance with rich information, high tissue separability and short computational time. These methods yield a significant discrimination between biological tissues.

CONCLUSION

We developed a hyperspectral imaging system in order to enhance some biological tissue visualization. The proposed methods provided an acceptable trade-off between the evaluation criteria especially in SWIR spectral band that outperforms the naked eye's capacities.

Hyperspectral imaging of melanocytic lesions

Hyperspectral imaging (HSI) allows the identification of objects through the analysis of their unique spectral signatures. Although first developed many years ago for use in terrestrial remote sensing, this technology has more recently been studied for application in the medical field. With preliminary data favoring a role for HSI in distinguishing normal and lesional skin tissues, we sought to investigate the potential use of HSI as a diagnostic aid in the classification of atypical Spitzoid neoplasms, a group of lesions that often leave dermatopathologists bewildered. One hundred and two hematoxylin and eosin-stained tissue samples were divided into 1 of 4 diagnostic categories (Spitz nevus, Spitz nevus with unusual features, atypical Spitzoid neoplasm, and Spitzoid malignant melanoma) and 1 of 2 control groups (benign melanocytic nevus and malignant melanoma). A region of interest was selected from the dermal component of each sample, thereby maximizing the examination of melanocytes. Tissue samples were examined at ×400 magnification using a spectroscopy system interfaced with a light microscope. The absorbance patterns of wavelengths from 385 to 880 nm were measured and then analyzed within and among groups. All tissue groups demonstrated 3 common absorbance spectra at 496, 533, and 838 nm. Each sample group contained at least one absorption point that was unique to that group. The Spitzoid malignant melanoma category had the highest number of total and unique absorption points for any sample group. The data were then clustered into 12 representative spectral classes. Although each of the sample groups contained all 12 spectral vectors, they did so in differing proportions. These preliminary results reveal differences in the spectral signatures of the Spitzoid lesions examined in this study. Further investigation into a role for HSI in classifying atypical Spitzoid neoplasms is encouraged.

Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates. Part 1. Developing blood stain discrimination criteria

In this article, we establish blood stain detection criteria that are less substrate dependent for use in a liquid crystal tunable filter-based multispectral-imaging system. Kubelka-Munk (KM) theory is applied to transform the acquired stains' reflectance spectra into the less substrate dependent spectra. Chosen spectral parameters are extracted from the KM absorbance spectra of several stain samples on several substrates. Blood discrimination criteria based upon those spectral parameters are then established from empirical data, tested, and refined. In our newly invented method, instead of introducing conventional contrast enhancement on the blood stain image, blood stain determination is executed mathematically via Boolean logic, resulting in more discriminative blood stain identification. This proposed approach allows for nondestructive, quick, discriminative, and easy-to-improve presumptive blood stain detection. Experimental results confirm that our blood stain discrimination criteria can be used to locate blood stains on several construction materials with high precision. True positive rates (sensitivity) from 0.60 to 0.95 are achieved depending on blood stain faintness and substrate types. Also, true negative rates (specificity) between 0.55 and 0.96 and identification time of 4-5 min are accomplished, respectively. The established blood stain discrimination criteria will be incorporated in a real blood stain detection system in part 2 of this article, where system design and considerations as well as speed issues are discussed.

Tongue tumor detection in medical hyperspectral images

A hyperspectral imaging system to measure and analyze the reflectance spectra of the human tongue with high spatial resolution is proposed for tongue tumor detection. To achieve fast and accurate performance for detecting tongue tumors, reflectance data were collected using spectral acousto-optic tunable filters and a spectral adapter, and sparse representation was used for the data analysis algorithm. Based on the tumor image database, a recognition rate of 96.5% was achieved. The experimental results show that hyperspectral imaging for tongue tumor diagnosis, together with the spectroscopic classification method provide a new approach for the noninvasive computer-aided diagnosis of tongue tumors.

Scattering characterization of nanopigments in metallic coatings using hyperspectral optical imaging

We have determined the reflectance spectra of colored metallic coatings with high spatial resolution by using a hyperspectral imaging system. Reflectance spectra were converted to color coordinates revealing characteristic color maps in the color space. Principal-component analysis was applied to decorrelate the spatial variability of the reflectance spectra. We found that the eigenvalue spectra follow different power laws. The scaling exponent was analyzed by considering random-walk-type processes. An estimation of the Hurst exponent was done, suggesting anomalous diffusion from multiple light scattering. The results show that hyperspectral imaging combined with principal-component analysis provides a valuable method for nondestructive testing of complex turbid media.